Opaque banks, price discovery, and financial instability

J. Finan. Intermediation 21 (2012) 383–408

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J. Finan. Intermediation j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j fi

Opaque banks, price discovery, and financial instability Jeffrey S. Jones a,⇑, Wayne Y. Lee b, Timothy J. Yeager c a

Breech School of Business, Drury University, Springfield, MO 65802, United States Alice L. Walton Chair in Finance, Sam M. Walton College of Business, University of Arkansas, Fayetteville, AR 72701, United States c Arkansas Bankers Association Chair, Sam M. Walton College of Business, University of Arkansas, Fayetteville, AR 72701, United States b

a r t i c l e

i n f o

Article history: Received 8 October 2010 Available online 31 January 2012 Keywords: Banks Opacity Merger Intra-industry revaluation Financial crisis

a b s t r a c t Opacity fosters price contagion that exacerbates the speculative cycles of bubbles and crashes that create financial instability. We find that banks with larger investments in opaque assets benefitted more from intra-industry revaluations associated with announcements of mergers in the period 2000–2006. The findings are robust to controls for competitive effects, spillover effects from higher likelihood of takeover, changes in real estate prices, and interest rates. Non-merger banks that gained most from merger activities also experienced the largest price declines during the subsequent 2007–2008 financial crisis. Ó 2012 Elsevier Inc. All rights reserved.

1. Introduction Opacity is the uncertainty that even the most sophisticated investors face in accurately assessing the fundamental value of a firm. Opacity can result when a firm chooses to withhold information from investors, which creates information asymmetry. But even full disclosure may not eliminate opacity if disclosure is not credible or is such that investors interpret the enigmatic quality of the information in contradictory ways. Furthermore, the inherent complexity of a business and the nature of the underlying assets can also contribute to opaqueness. To some degree, all firms are opaque, and Morgan (2002) shows that banks are relatively more opaque than industrial firms. But the relative opacity of banks is a matter of some debate (Flannery et al., 2004), and though interesting, detracts from the particular concern of opacity for banks. As important suppliers of credit to the economy, deposit insurance and conscientious regulatory oversight are necessitated even when banks are only modestly opaque. Markets may not be completely ⇑ Corresponding author. Address: 900 N. Benton Ave., Springfield, MO 65803, United States. Fax: +1 417 873 7357. E-mail addresses: [email protected] (J.S. Jones), [email protected] (W.Y. Lee), [email protected] (T.J. Yeager). 1042-9573/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.jfi.2012.01.004

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effective in disciplining the actions of bank managers.1 Managers have the ability to rapidly transform liquid bank assets, increasing the uncertainty about the underlying profitability and risks of the firm (Myers and Rajan, 1998). More importantly, opacity has potential to threaten the banking system because it fosters conditions that lead to price contagion and engenders an environment prone to financial instability and systemic risk. Fundamentally, opacity contributes to this environment for two reasons. First, opacity limits informed arbitrage, the absence of which creates space for noise trading (DeLong et al., 1990). The variability in noise traders’ beliefs about risk increases price uncertainty, requiring sophisticated investors (arbitrageurs) to bear greater risk. In addition, positive price feedback that reinforces the misperceptions of noise traders can prolong deviations from fundamentals. To the extent that sophisticated investors are risk averse, the high risk and potential ruin from the accumulation of short-term losses reduce their willingness to bet against noise traders,2 allowing speculative bubbles to develop. Eventually market (or regulatory) forces will deflate bubbles, often resulting in a rapid and substantial decline in price. In the banking industry, a rapid and substantial fall in equity prices is particularly problematic because it makes it more difficult for banks to raise capital, reducing banks’ willingness and ability to lend. Disruptions in the supply of credit turn a financial crisis into an economic crisis (Bernanke, 1983; Calomiris and Mason, 2003; Flannery, 1998). Dell’Ariccia et al. (2008) show that sectors more dependent on external finance perform relatively worse during banking crises. Second, opacity makes it more likely that even informed investors will use bank-specific information to influence the valuations of other banks. If bank-specific information results in positive valuation effects for the bank, managers of other banks may seek to emulate the strategies of the highervalued banks, creating a propensity for herding and overinvestment by bank managers. Rajan (1994) develops a model based on earnings signals rather than equity signals. He shows how the interdependence of bank credit policies tends to increase business cycle volatility through an oversupply of credit during expansions and an undersupply of credit during contractions. In this framework, managers have short-term concerns and value their market-perceived reputations, and their reputation is conveyed to the market primarily through bank earnings. Managers of banks with deteriorating loans can postpone revealing this to the market by increasing loan volume, which generates profitable upfront fees and boosts earnings. But when banks begin to report lower earnings, all banks simultaneously tighten their credit policies and decrease loan volume. We examine the impact of opaqueness on price contagion in the banking industry in the years 2000– 2006 that precede the 2007 financial crisis. In general, contagion arises from the propagation of asymmetric information when investors cannot distinguish between bank-specific and systematic events (Diamond and Dybvig, 1983). The bank-specific catalyst around which we measure price contagion in the industry is the announcement of a bank merger. We choose to examine merger announcements for two reasons. First, they are a common, easily identifiable event during our sample period. Second, merger announcements contain measurable firm-specific information (the size of the premium) about the value of a bank (the target), which is provided by another firm in the industry (the bidder). Our choice of event, however, is not meant to suggest that merger announcements are an exclusive catalyst for intra-industry contagion. Merger announcements are generally considered a positive signal of value, but other positive bank-specific events, such as positive earnings surprises or increases in dividends, can yield similar effects. Moreover, negative bank-specific events such as bank failures (Aharony and Swary, 1983), bankruptcy announcements (Lang and Stulz, 1992), dividend reductions (Slovin et al., 1999), seasoned equity offerings (Slovin et al., 1992), or negative earnings surprises (Prokopczuk, 2009) can also give rise to industry contagion, but these types of events were relatively infrequent in the 2000–2006 period. Our study builds on Eckbo (1983, 1985), Stillman (1983) and Akhigbe and Madura (1999). Two competing hypotheses are offered to explain intra-industry revaluations when a merger is announced.

1 Evidence on the ability of markets to discipline banks is mixed. Some examples are Flannery and Sorescu (1996), Morgan and Stiroh (2001), Bliss and Flannery (2002), and Goyal (2005). 2 This may be especially true for younger fund managers, as suggested by Chevalier and Ellison (1999). Rajan (2005) suggests that herding by investment managers ensures that one will not underperform his peers and herding in any form has the potential to drive asset prices away from fundamentals.

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The competitive effect predicts a negative industry reaction because the merged firm is larger and has more market power. The spillover effect predicts positive industry revaluations because of an increased probability that other firms in the industry may become targets in the future. The Akhigbe and Madura (1999) study, which focuses on the banking industry over the period 1983–1996, finds positive revaluation effects for rival banks located in the same state as the target.3 The positive revaluations, which are higher when the performance of the target bank is better, are not consistent with heightened competition from merged banks that are financially stronger entities. Rather, the revaluations, which are higher for rival banks that are smaller and have poorer performance, appear to reflect a spillover associated with an increased probability of takeover. In particular, rival banks acquired in the following 2 years exhibit higher revaluations. In this paper, we consider opacity as an alternative explanation for positive intra-industry revaluations surrounding bank merger announcements. Following Morgan (2002), our primary measure of opacity is bank asset composition. For robustness, we also consider alternative opacity measures, specifically the frequency and magnitude of split bond ratings. We empirically document the effects of opacity on bank valuations and systemic risk in two complementary ways. First, we assess the impact of a bank’s opaqueness on its share price around the announcements of bank mergers in the period 2000–2006. We find that, on average, merger announcements convey positive information about the values of other banks. To our point, the cumulative abnormal returns (CAR) of banks not directly involved in the merger are higher for banks with larger investments in opaque assets. The use of alternative measures of opacity confirms this finding. Our results are robust to adjustments for competitive effects from the possibility of both heightened competition and spillover effects associated with a higher probability of takeover, as well as favorable economic factors such as changes in real estate prices and the level of interest rates. Second, we show that relatively more opaque banks, which benefitted most from revaluations around merger activity in 2000–2006, also experienced the largest share price declines with the onset of the 2007 financial crisis. Overall, banks increased investments in opaque assets over the period 2000–2006, but the increase is not uniform across all banks. We find evidence of a feedback effect in response to the revaluations around merger announcements, as the less opaque banks with the lowest revaluations in the early years of the sample sought to emulate the strategies of the more opaque banks with the highest revaluations by making significantly greater investments in opaque assets throughout 2000–2006. The increasing levels of opaque assets likely magnified the eventual decline in equity share prices in 2007–2008. Additionally, bank equity returns exhibit higher systematic risk and lower idiosyncratic risk as the proportion of opaque assets increases. The policy implications of our findings are twofold. Because opacity contributes to speculative bubbles and increases the likelihood of financial crises, policy makers may wish to implement regulations specifically designed to reduce the degree of opacity in financial intermediaries. Such policies can take the form of enhanced investor disclosure and/or more stringent limits on asset composition and complexity. Further, because opacity also fosters intra-industry contagion, our results provide strong support for regulatory efforts that focus on system-wide supervision in addition to individual bank supervision. The remaining sections of the paper are organized as follows. Section 2 provides a summary of prior literature related to bank opacity. Section 3 discusses the data used in the study. Empirical results for intra-industry revaluations in good economic times and periods of financial crisis are reported in Sections 4 and 5, respectively. Section 6 concludes.

2. Background and research hypotheses In their function as financial intermediaries, banks have relatively small investments in physical assets. Loans, which are the primary assets for most banks, are inherently opaque (Campbell and Kracaw, 1980; Berlin and Loeys, 1988). Because loans are privately negotiated transactions between a bank and borrower, banks have privileged information regarding the characteristics of the loan contract and the 3 The competitive environment for banks changed dramatically with the passage of the Riegle–Neal Act in 1994, which eliminated virtually all restrictions on interstate banking.

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creditworthiness of its borrowers. This informational asymmetry between borrowers and lenders is, Leland and Pyle (1977) note, a primary reason for the existence of financial intermediaries. Trading assets, which consist mostly of securities and derivative instruments that a bank intends to buy or sell on an ongoing basis, also contribute to opacity. Like loans, trading assets are held almost exclusively by banks, though they are concentrated primarily at the largest banks. But the source of opacity for trading assets differs. Loans are typically held-to-maturity investments and the source of opacity is unknown credit risk – the ability and willingness of the borrower to repay. For trading assets, which are typically very short-term investments, liquidity risk rather than unknown credit risk is the market’s primary concern. On one hand, when markets are functioning properly, the liquidity risk of trading assets arises because the composition of trading assets can change very quickly between quarterly reporting dates. It can be difficult to tell what trading activity occurs throughout a given quarter – a characteristic that Morgan (2002) calls slippery. The unusually liquid nature of trading securities subjects outside investors to transformation risk because it does not force bank managers to make credible commitments to investment strategies that protect outside investors (Myers and Rajan, 1998). Consequently, liquidity can limit the ability of banks to raise capital and their capacity to absorb economic shocks. Moreover, investments in liquid assets also reduce the amount of illiquid assets that are financed by relatively liquid liabilities. Banks are less susceptible to runs when deposit insurance is incomplete and the severity of potential losses associated from unplanned dispositions of illiquid assets is diminished. Liquidity, however, discourages the monitoring of borrowers by banks and reduce bank equity values (Berger and Bouwman, 2009), which both contribute to the fragility of banks. On the other hand, when markets cease to function properly, trading assets can become illiquid. In this situation, accounting rules require that trading assets are marked-to-market on the balance sheet, and this market value is usually determined by market transactions involving similar securities.4 When markets for trading assets cease to function and dry up, as happened with mortgage-backed securities in the 2007 financial crisis, it becomes difficult to ascertain the true intrinsic value of these securities based on observed market prices. As a result, banks are forced to model-driven methods for estimating market value. In such an environment, accountants and auditors typically push for very conservative estimates of market value, which can result in large accounting losses that erode the level of bank capital. The amount of loans and trading assets held by banks directly impact the incidence and magnitude of split ratings on new bond issues by banks (Morgan, 2002). Split ratings reflect disagreement by rating agencies and proxy for the information uncertainty associated with opacity. Morgan (2002) also finds that the likelihood for split ratings and divergence in rating disagreements is higher for banks than for a matched sample of non-banks. Using a sample of only non-financial firms, Livingston et al. (2007) confirm that split ratings reflect firm opacity. Flannery et al. (2004) find, however, that the market microstructure characteristics of banks are no different than those of industrial firms. Moreover, analysts’ forecasts of earnings appear to be more accurate for banks, suggesting that banks are perhaps less opaque. But improved accuracy of analyst forecasts may simply indicate the ability of banks to manage earnings (Beatty et al., 2002). And in Flannery et al. (2010), a dramatic shift in market microstructure characteristics coincided with increased bank opacity in the 2007 financial crisis. We hypothesize that opacity fosters intra-industry price contagion that contributes to speculative bubbles and increases the likelihood of financial crises. Investors confuse bank-specific events with economy-wide events when they cannot distinguish across banks. The resulting volatility in share prices exacerbates credit cycles because share prices affect bank capital, and thereby, banks’ willingness and ability to lend, turning a financial crisis into an economic crisis.

3. Data sources and descriptive statistics Our first objective is to assess the impact of a bank’s opaqueness on its share price revaluation around bank mergers between 2000 and 2006. We compile data on merger activity and compute 4

See FASB statement 157 Fair VALUE measurements.

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abnormal returns and opacity measures both for merger banks and non-merger banks at the time of merger announcements. The target and bidder banks are collectively referred to as the merger banks. All other banks are considered non-merger banks when a merger is announced, with three exceptions. First, bidder banks are excluded from the non-merger bank sample over a ±30-calendar day window around the date the acquisition is announced. Second, target banks are excluded from the non-merger bank sample 30 calendar days prior to the merger announcement and thereafter. Third, a bank’s equity shares must be continuously traded 1 year prior to the merger announcement to be included. This restriction ensures that the number of trading days is sufficient to accurately compute an abnormal return over the event window. 3.1. Non-merger bank sample Our non-merger bank sample consists of publicly traded domestic bank or financial holding companies in the Center for Research in Security Prices (CRSP) listed SIC codes 6020-6030, 6712, or 6719 for which FR Y-9C data were available for the years ended 2000 through 2006. Because the reporting threshold for the FR Y-9C was raised from $150 million to $500 million in 2006, we excluded banks with inflation-adjusted assets less than $500 million to ensure consistency. We use December 31, 2006 as the base period and the GDP deflator to correct for inflation. Banks whose stock did not have positive trading volume in at least 85% of the days for which data were available from CRSP were excluded. 3.2. Merger bank sample We identified announcements of successfully completed domestic mergers and acquisitions involving publicly traded banks over the period 2000 through 2006 where the bidder gained 100% control over the target from the Thomson Financial SDC Platinum Mergers and Acquisitions Database (SDC).5 From this initial list, data from CRSP and FR Y-9C for both target and bidder banks were available for 94 announcements. We excluded two transactions that were flagged as a rumored deal prior to the actual announcement; four small mergers where the inflation-adjusted asset size of the target was below $500 million; and four where the target did not have CRSP or FR Y-9C data at least 1 year prior to the merger announcement. In addition, there were four situations where two mergers were announced on the same day. In these cases, we retained the merger transaction that involved the larger target bank resulting in a final sample of 80 merger announcements.6 3.3. Descriptive statistics We compute asset composition variables and other operating characteristics of the sample banks. We recognize that the source of opacity may differ across assets. However, we are not concerned with these differences as much as we are with capturing the effect of opacity on the magnitude of industry revaluations. Accordingly, we proceed with a simple categorization of assets into four categories: real estate loans, all other loans, all other opaque assets, and transparent assets. Balance sheet variables are quarter end prior to the merger announcements and income statement variables are annualized quarterly amounts from the same time period. Appendix A provides a description of the variables and an explanation of how they are constructed. Non-merger banks are compared to merger banks in Table 1. There are a total of 19,814 bank-quarter observations in the non-merger bank sample, with an average of 247 bank-quarter observations at 5 We start with target SIC codes in SDC that might reasonably indicate a BHC or FHC: 6021, 6022, 6029, 6712, and 6719. A few BHC targets (four) were also discovered in SDC under SIC code 6036. 6 Although our time period and selection criteria differ somewhat from other recent studies on BHC mergers, our resulting sample is similar in size relative other studies such as Anderson et al. (2004). Moreover, our selection criteria exclude any transactions between a BHC and other types of financial institutions (such as an investment bank or finance company). The passage of the Gramm–Leach–Bliley Act in 1999 may have increased the number of mergers between BHCs and other types of financial institutions in our sample time period and possibly reduced the number of mergers between only BHCs relative to earlier time periods.

388

Assets ($million)

Equity M/B

Asset composition Real estate loans

Other loans

Other opaque assets

Transparent assets

Profitability

Operating attributes

Non-merger banks Mean Median Std. deviation N

25,499 2343 119,092 19,654

1.88 1.79 0.81

44.44 44.74 14.44

20.41 19.77 10.32

8.04 6.68 5.44

27.11 25.86 11.13

1.75 1.77 1.02

Merger banks Mean Median Std. deviation N

48,371 4910 146,567 160

1.92 1.84 0.70

43.02 44.66 13.22

21.46 20.08 10.10

9.18 7.64 5.96

26.34 25.62 9.21

Difference Mean Median

22,872 2567

0.04 0.05

1.42 0.08

1.05 0.31

1.14 0.96

0.77 0.24

Non-perf. loans

Core deposits

Capital

Non-interest income

0.49 0.39 0.42

61.02 61.71 12.39

9.14 8.95 2.19

1.51 1.16 1.53

1.80 1.87 0.71

0.48 0.43 0.31

59.46 59.52 11.56

9.45 9.27 2.25

1.65 1.26 1.45

0.05 0.10

0.01 0.04

1.57 2.20

0.31 0.32

0.14 0.10

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Table 1 Non-merger vs. merger banks. This table compares the asset composition and operating attributes of non-merger banks to bidder and target banks in a sample of 80 mergers over the period 2000–2006. Only banks with inflation adjusted assets greater than $500 million (base period of December 2006) are included. A bank that was a bidder is excluded as a non-merger bank in a ±30 calendar day window surrounding the merger announcement date. A bank that was a target is excluded as a non-merger bank 30 days prior to the merger announcement and thereafter. All variables are quarter end at least 30 days prior to the merger announcement. Asset composition and operating attributes are expressed as a percent of total assets, with income statement items presented as annualized quarterly amounts. Profitability is defined as earnings before taxes and extraordinary items. ,, denotes statistical significance at the 1%, 5%, and 10% levels, respectively based on a standard t-test for differences in means and a Wilcoxon test for differences in medians.

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each merger announcement. The minimum (maximum) number of non-merger banks at a given merger event is 230 (258), involving 357 unique banks across all 80 merger announcements. For the sample of merger banks there are 160 bank-quarter observations across 80 merger announcements that involve 49 unique acquiring banks because some banks were multiple bidders. But since a bank can only be a target once, there are exactly 80 unique target banks.7 Merger banks do not appear to differ significantly from non-merger banks, with two exceptions. The first is size. In terms of assets, merger (bidder/target) banks are, on average, roughly two times larger than non-merger banks. This size difference is driven primarily by bidders rather than by targets. The significantly skewed distribution of assets across merger as well as non-merger banks confirms a well-known fact – there are a few very large banks and numerous small banks.8 The second is that merger banks hold significantly more other opaque assets. Core deposits and noninterest income are also slightly lower and higher, respectively, for merger banks. Table 2 shows there are significant differences between bidders and targets. Bidder banks are much larger than target banks and have higher market-to-book equity multiples. Bidder banks also hold less real estate loans and more other loans and other opaque assets. In contrast, targets hold slightly more transparent assets although the difference is not statistically significant. The differences in asset composition are not surprising and likely driven by the difference in size between bidders and targets. Larger banks are more likely to be involved in credit card lending (included in other loans) and trading activities (included in other opaque assets). The relative propensity for large banks to be involved in such activities reduces the amount of real estate lending. Overall, bidders appear more opaque than targets. In addition to differences in asset composition, note also that bidder banks are more profitable and have higher amounts of noninterest income. Target banks, in contrast, have significantly more core deposits than bidder banks. Access to valuable core deposits of targets may be a possible motivation for acquisition. Descriptive statistics on merger deal characteristics are presented at the bottom of Table 2. The premium paid to the target shareholders is calculated as follows:

PREMIUM ¼

P  P28 P28

ð1Þ

where P is the share price paid to target bank shareholders and P28 is the target bank share price 28 calendar days prior to the announcement date.9 The mean (median) premium is 33.03% (27.91%). Shareholders of target banks receive a substantial takeover premium. We calculate the CAR for both the target and bidder banks around the merger announcement. The combined announcement returns are weighted based on the market values of equity at quarter end prior to the announcement. The returns generating model to compute abnormal returns for targets and bidders utilizes the three Fama–French (1992) risk factors plus the Fama–French price momentum factor10 and is estimated over trading days 312 to 60 (a period of approximately 1 year) prior to the merger announcement date. The event window is an 11-day period defined by ±5 trading days around the merger announcement date. Daily abnormal returns are computed and summed over the event period to determine the CAR. Consistent with prior research on the announcement period returns of bank mergers (Houston and Ryngaert, 1994; DeLong, 2001), the mean (median) cumulative abnormal return of targets is 22.48% (18.45%) and significantly positive at the 1% level, while the mean (median) cumulative abnormal return of bidders is near zero at 1.08% (0.47%).11 The significantly positive mean (median) combined 7 In unreported robustness tests, we examine the sensitivity of our results across events that involve serial ‘repeat’ acquirers compared to non-serial ‘one-time’ acquirers. 8 Small publicly traded banks can be bidders and targets and are included in the merger and non-merger bank samples if their asset size exceeds the $500 million threshold. 9 The stock price 28 days prior to the announcement date is obtained from SDC and verified in CRSP. 10 The Fama–French momentum factor (UMD) is computed in a manner similar to that of Carhart (1997). 11 Though beyond the scope of this paper, we find in an unreported analysis that returns to the bidder are lower when the bidder has relatively small holdings of transparent assets and pays mostly with equity. Conversely, a bidder with small holdings of transparent assets that pays mostly with cash experiences higher announcement returns. This is consistent with the findings of Moeller et al. (2007) and suggests that bank asset composition reflects opacity.

390

Assets ($million)

Equity M/B

Asset composition

Operating attributes

Real estate loans

Other loans

Other opaque assets

Transparent assets

Profitability

Non-perf. loans

Core deposits

Capital

Non-interest income

Bidder banks Mean Median Std. deviation N

83,883 21,306 196,178 80

2.11 1.97 0.76

40.97 42.76 10.82

23.17 21.52 8.77

10.58 9.11 6.53

25.27 24.55 9.12

2.09 2.09 0.62

0.45 0.41 0.25

56.97 55.53 10.85

9.72 9.61 2.06

1.97 1.49 1.41

Target banks Mean Median Std. deviation N

12,859 1437 46,860 80

1.73 1.67 0.58

45.06 46.21 15.04

19.75 18.10 11.06

7.78 6.58 4.99

27.41 26.03 9.24

1.51 1.53 0.68

0.50 0.45 0.36

61.95 63.56 11.77

9.18 8.73 2.42

1.33 1.01 1.41

2.80 2.53

2.14 1.48

0.58 0.56

0.05 0.04

0.54 0.88

0.64 0.48

Difference Mean Median

71,024 19,869

0.38 0.30

4.09 3.46

3.42 3.42

4.98 8.03

Deal characteristics

Mean

Median

Standard deviation

Premium paid Target CAR, % Bidder CAR, % Combined CAR, % % Cash > 75% % Tender offers

33.03 22.48 1.08 1.57 10.00 1.25

27.91 18.45 0.47 0.89

23.43 16.69 5.66 5.04

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Table 2 Bidder vs. target banks. This table compares bidder and target banks in a sample of 80 mergers over the period 2000–2006. Only banks with inflation adjusted assets greater than $500 million (base period of December 2006) are included. All variables are quarter end at least 30 days prior to the merger announcement. Asset composition and operating attributes are expressed as a percent of total assets, with income statement items presented as annualized quarterly amounts. Profitability is defined as earnings before taxes and extraordinary items. Summary statistics of selected deal characteristics are shown at the bottom of the table. The premium paid is calculated as a percent of the target stock price 28 calendar days prior to the announcement date. The CAR of the bidder and target banks are calculated using daily returns, an estimation period 312 to 60 trading days prior to the merger announcement date, and an event window ±5 days around the merger announcement date. The return generating model utilizes the 3-factor Fama–French (1992) model, plus a momentum factor. The CRSP equal-weighted index (less the riskfree rate) is used as the proxy for the market factor. The combined CAR for the bidder and target bank is the weighted average announcement CARs of the bidder and target bank based on their relative market values. , ,  denote statistical significance at the 1%, 5%, and 10% levels, respectively based on a standard t-test for differences in means and a Wilcoxon test for differences in medians.

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announcement return of 1.57% (0.89) is consistent with shareholder value creation. A majority of the mergers in the sample are geographic focused, which DeLong (2001) suggests, increases the potential for value creation. Lastly, Table 2 also shows that bank mergers are predominantly friendly – only 1 of 80 mergers was through a tender offer, so we exclude this variable from further analysis. Moreover, in 10% of the mergers, the method of payment is at least 75% in cash. 4. Revaluations surrounding mergers In this section, we compute the intra-industry revaluation surrounding mergers. We then show that the intra-industry revaluation is positively related to the opacity of the non-merger banks even after controlling for merger-specific characteristics, competitive effects, and spillover effects. 4.1. Intra-industry revaluation Computing the cumulative abnormal returns of the non-merger banks captures the intra-industry revaluation associated with merger announcements. Estimating the cumulative abnormal return for each non-merger bank associated with all the merger announcements over the entire sample period using a traditional event study approach that divides returns into an estimation period and event window for each merger announcement is problematic because merger announcements throughout the sample period overlap. Instead, we estimate a single daily returns regression for each bank over the period 1999–2006 with daily dummy variables to capture the abnormal returns around an 11-day event window defined by ±5 trading days around each merger announcement date.12 The three Fama–French (1992) factors, a price momentum factor, changes in real estate prices, and the level of interest rates, are used as risk proxies. The time-series model is:

rt ¼ a þ b1 MKT t þ b2 SMBt þ b3 HMLt þ b4 UMDt þ b5 CS PCT t þ b6 FEDFUNDt þ b7 EVENT t þ et ð2Þ where MKT is the daily equal-weighted CRSP index13 minus the risk free rate, SMB is the Fama–French daily size factor, HML is the Fama–French daily value factor, UMD is the Fama–French daily price momentum factor, CS_PCT is the percentage change in the Case-Shiller Composite 10 index, FEDFUND is the daily federal funds rate, and EVENT is a vector of daily dummy variables that takes on a value of 1 if day t is within ±5 trading days of a merger announcement. The estimated coefficients for EVENT capture the daily abnormal returns associated with merger announcements. For a non-merger bank that does not trade over the entire 8-year period, the estimation period is reduced to the actual number of days available. A bank must trade continuously at least 1 year prior to a merger announcement, however, before an abnormal return is computed for that event. If a particular day falls within more than one merger announcement event window, then the estimated coefficient of EVENT is allocated equally across the merger event windows on that day to avoid doublecounting the abnormal return.14 The CAR for each non-merger bank around merger announcements is computed as the sum of the (allocated) daily abnormal returns over 11-day event windows. Summary statistics for intra-industry revaluations (expressed as percentages) associated with merger announcements are shown in Table 3 over the entire sample period, two equal length sub-periods, and by calendar year. The results reported in Table 3 are Winsorized using 1% and 99% levels of the 12

An alternative event window of ±3 trading days produces similar results. Fama–French (1992) use a value-weighted index as a proxy for the market. We chose the equal-weighted index instead of the value-weighted index because the banking industry was becoming an increasingly larger part of the value-weighted index during this time period. 14 Alternative methods of allocating the daily abnormal return for overlapping events were considered, such as the size of the target, the distance (number of days) from the actual merger announcement, or the combined announcement return of the target and bidder. Because it is a priori difficult to justify any particular one method of allocation over another, we adopted an equal allocation rule. 13

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Table 3 Intra-industry revaluations surrounding bank merger announcements. This table summarizes the intra-industry revaluations of non-merger banks surrounding merger announcements in 2000–2006. The return generating model used to compute abnormal returns utilizes the 3-factor Fama–French (1992) model with additional factors for price momentum, changes in real estate prices and the level of interest rates. The equal-weighted CRSP index (less the risk-free rate) is used as the market proxy and the return generating model is estimated over the period 1999–2006 using daily returns for each non-merger bank in the sample. The event window is ±5 trading days around the merger announcement date. Daily dummy variables are used to capture the daily abnormal return on days that fall inside a merger announcement event window. If a given day was part of more than one merger announcement event, the abnormal return associated with that day is allocated equally across the merger announcement events. The cumulative abnormal returns (CAR) for the non-merger banks are computed as the sum of the daily abnormal returns over the 11-day event window. The values reported in the table reflect non-merger bank CARs associated with each merger announcement Winsorized at the 1% and 99% levels. Weighted-average intra-industry revaluations across the 80 merger announcement events are presented as value-weighted based on total assets and as precision-weighted based on the inverse of the average standard error of the estimated daily abnormal returns for each non-merger bank from the estimated 6-factor return generating model. , ,  denotes statistical significance at the 1%, 5%, and 10% levels, respectively based on a standard t-test for differences in means and a Wilcoxon test for differences in medians. Number of mergers

Number of revaluations

Intra-industry revaluation Weighted averages Mean

Median

Standard deviation

Value weight

Precision weight

Total January 2000–June 2003 July 2003–December 2006

80 39 41

19,174 9538 9636

0.823 1.356 0.296

0.414 0.880 0.161

4.100 5.013 2.832

0.980 1.714 0.283

0.828 1.380 0.304

Each year 2000 2001 2002 2003 2004 2005 2006

16 12 7 15 14 11 5

3899 2957 1713 3624 3329 2505 1147

1.614 0.637 2.759 0.026 0.092 0.104 1.937

1.155 0.371 2.501 0.006 0.013 0.233 1.827

5.975 3.805 4.879 2.471 2.867 2.922 3.374

3.003 0.207 2.608 0.405 0.089 0.343 2.140

1.662 0.622 2.811 0.017 0.108 0.090 1.961

CARs at each merger announcement. The last two columns report the average values of a valueweighted and precision-weighted CAR. Across all events, the overall intra-industry revaluation is positive with a mean (median) of 0.823% (0.414%), both of which are significantly different from zero at the 1% level. The magnitudes of intraindustry revaluations vary with time. Intra-industry revaluations in the first half of the sample period are much higher than in the second half of the sample period, driven primarily by the years 2000 and 2002. Moreover, though intra-industry revaluations in the latter half of the sample period appear to be much lower, the second largest intra-industry revaluation occurred in 2006. The magnitude and spacing of the merger announcements is shown in Fig. 1. Triangular boxes represent the median intra-industry revaluations and bar heights reflect the difference between the 75th and 25th percentiles. Merger announcements occur in clusters and this is especially true in the latter part of 2003 through the early part of 2004. Most of the median intra-industry revaluations are positive, but note that not all merger announcements are associated with a positive intra-industry revaluation. Fig. 2, which presents the histogram of the median intra-industry revaluations at each of the 80 merger announcements, has a distinct right skew. 4.2. Univariate analysis: intra-industry revaluation and opacity Having shown that bank merger announcements create positive intra-industry revaluations, we now examine the relationship between the asset composition of non-merger banks and the magnitude of their revaluations. We rank the non-merger banks both into quartiles based on the percent of transparent assets held by the bank. The ranking procedure is implemented at each merger announcement. The mean, median, and standard deviation of the non-merger bank CARs are computed for each quartile and presented in Panel A of Table 4.

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393

10

Cumulative Abnormal Return (%)

8 6 4 2 0 -2 -4 -6 -8 -10

Fig. 1. Intra-industry revaluations surrounding merger announcements. The figure above graphs the magnitude (median) and dispersion of intra-industry revaluations surrounding each of the 80 merger announcements over the period 2000–2006. Triangular boxes represent the median intra-industry revaluations and the heights of the bars indicate the dispersion between the 25th and 75th percentiles.

Fig. 2. Histogram of median intra-industry revaluations. The histogram above shows the median intra-industry revaluations for non-merger banks surrounding each of 80 merger announcements over the period 2000–2006. The curve represents a normal distribution with zero mean and volatility equal to the standard deviation of the 80 median intra-industry revaluations.

We expect opacity to be positively related to the magnitude of revaluation, and the results in Table 4 support this conjecture. The non-merger bank quartile with the most opaque (fewest transparent) assets has a mean (median) intra-industry revaluation of 0.985 (0.496) compared to 0.754 (0.374)

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Table 4 Univariate analysis: intra-industry revaluations and opacity. This table examines the impact of bank opacity on intra-industry revaluations surrounding merger announcements. Panel A reports summary statistics for the intra-industry revaluations across non-merger banks sorted by quartiles at each merger announcement event based on the percentage of transparent assets held by non-merger banks. Panel B reports summary statistics for the cumulative intra-industry revaluations of non-merger banks over the entire sample period (2000–2006) as well as over the first-half (January 2000–June 2003) and second-half (July 2003–December 2006) of the sample period. Quartiles for the cumulative intra-industry revaluation are based on the average opacity ranking of non-merger banks over the specified time periods. , ,  denote statistical significance at the 1%, 5%., and 10% levels, respectively, based on a standard t-test for differences in means and a Wilcoxon test for differences in medians. Quartile

Number of revaluations

Mean

Median

Standard deviation

4766 4798 4835 4775

0.985 0.850 0.706 0.754 0.231

0.496 0.424 0.344 0.374 0.122

4.100 4.124 4.071 4.101

Panel B: Cumulative intra-industry revaluations Entire period: January 2000–December 2006 Most opaque 1 31 2 31 3 32 Least opaque 4 32 Most opaque–least opaque

83.30 74.55 61.90 54.12 29.18

92.61 80.98 69.96 57.92 34.69

53.90 40.94 57.64 49.89

First-half: January 2000–June 2003 Most opaque 1 2 3 Least opaque 4 Most opaque–least opaque

43 44 46 43

70.07 55.62 56.80 44.03 26.04

71.78 62.35 64.53 43.18 28.60

35.29 37.16 38.35 33.12

Second-half: July 2003–December 2006 Most opaque 1 2 3 Least opaque 4 Most opaque–least opaque

43 43 44 42

19.24 12.36 14.63 8.88 10.36

19.53 16.65 15.43 11.26 8.27

22.43 27.55 26.86 24.19

Panel A: Intra-industry revaluations Most opaque 1 2 3 Least opaque 4 Most opaque–least opaque

respectively for the least opaque non-merger bank quartile. The mean (median) differences of 0.231 (0.122) are significant at the 1% level. We then examine the cumulative effect of intra-industry revaluations across all merger announcements. Results are presented in Panel B of Table 4 over the entire sample period (2000–2006) as well as for the first and second halves of the sample period. To qualify for inclusion, a bank must be a nonmerger bank throughout all the merger announcements in the indicated time period. The number of non-merger banks is reduced by roughly half for the full period and by one-third in the two sub-periods as a result of this restriction. For all three time periods, the cumulative intra-industry revaluations are highest among the quartile of most opaque non-merger banks, and lowest among the quartile of least opaque non-merger banks. There is a notable difference in the magnitude of revaluation across the two subsample periods – the cumulative intra-industry revaluation is clearly larger in the first half of the sample period, consistent with Table 3. The results are also economically significant. For example, the quartile difference in cumulative mean (median) intra-industry revaluation across all merger announcements is 29.18% (34.69%) over the entire sample period. The result from Table 4 is compelling initial evidence that intra-industry revaluations are positively related to a non-merger bank’s investments in opaque assets. The univariate results do not, however, control for characteristics other than opacity that also may influence revaluation – in particular, merger-specific characteristics; heightened competition; and spillover associated with an increased probability of takeover. We examine each of these in turn.

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Table 5 Intra-industry revaluations and merger-specific characteristics. This table examines the impact of merger deal characteristics on intra-industry revaluations surrounding merger announcements. Panel A divides based on the percentage of the total deal price paid to target shareholders in cash. Panel B categorizes the industry contagion based on whether the premium, as calculated relative to the stock price of the target 28 calendar days prior to the announcement, is above or below the sample median. Panel C divides the industry contagion based on the CAR of the target, the CAR of the bidder, and the combined announcement return of the target and bidder firms, which is computed as the weighted average CAR based on the relative market values of the target and bidder in the quarter preceding the merger announcement. , ,  denotes statistical significance at the 1%, 5%., and 10% levels, respectively, based on a standard t-test for differences in means and a Wilcoxon test for difference in medians. Number of mergers Panel A: method of payment P75% Cash 8 <75% Cash 72 Difference Panel B: size of target premium Above median 40 Below median 40 Difference Panel C: announcement CAR Target Above median 40 Below median 40 Difference

Number of revaluations

Mean

Median

Standard deviation

1939 17,235

0.840 0.821 0.019

0.308 0.426 0.118

4.000 4.111

9662 9512

0.942 0.703 0.239

0.525 0.300 0.225

4.325 3.856

9637 9537

1.154 0.490 0.664

0.676 0.229 0.447

4.699 3.357

Bidder Above median Below median Difference

40 40

9640 9534

1.116 0.527 0.589

0.498 0.323 0.175

4.949 4.083

Combined Above median Below median Difference

40 40

9647 9527

1.273 0.368 0.905

0.683 0.146 0.537

4.050 4.101

4.3. Control variables: merger-specific characteristics We consider the method of payment, size of the premium paid to target banks, and CARs of the target and bidder as possible relevant merger-specific characteristics. Loughran and Vijh (1997) find that when a bidder pays with cash, its abnormal returns around the announcement are higher. Stock payments can signal to the market that the bidder believes its shares are overvalued, and thereby, that other banks are similarly overvalued. We expect cash deals will create more positive intra-industry revaluations than non-cash deals. Panel A of Table 5 divides the sample of 80 merger announcements into deals where the payment is greater than 75% in cash and those where it was less that 75% in cash. Based on this categorization, there were 8 cash deals and 72 non-cash deals. The mean intra-industry revaluation for cash deals is slightly higher, but the median is slightly lower. Statistically, the differences are indistinguishable from zero. In most situations, the bidder is forced to pay a premium above current market price in order to gain control of the target. Akhigbe and Madura (1999) find that the degree of intra-industry revaluation is positively related to the CAR of the target bank. The CAR of the target bank can reflect new information not only about its own intrinsic value but also about the intrinsic values of other banks.15 We expect the intra-industry revaluation to be positively related to the size of the premium paid to the target bank. 15 The spillover associated with an increased probability of takeover can also be influenced by the size of the premium. Our categorization of the premium as a merger specific characteristic, however, is consistent with Akhigbe and Madura (1999).

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Table 6 Intra-industry revaluations and competitive effects. This table examines the impact of geographic and product focus on intraindustry revaluations surrounding merger announcements. Geography is defined by the Federal Reserve districts in which the bidder and target banks are located. An intra-district merger is considered geographically focusing, and an inter-district merger, geographically diversifying. Product focus is based on the differences in asset composition between bidder and target banks using a Herfindahl-Hirschman index calculated as the sum of the squared differences across the four asset categories. A lower (higher) value of the asset diversity index indicates more (less) similar asset composition between bidder and target banks. The merger is product focusing when the asset diversity index is below the merger bank group median, and product diversifying, when the asset diversity index is above the merger bank group median. , ,  denotes statistical significance at the 1%, 5%., and 10% levels, respectively, based on a standard t-test for differences in means and a Wilcoxon test for differences in medians.

Geographic focusing Product focusing Same district Different district Difference Product diversifying Same district Different district Difference Product focusing–product diversifying Geographic diversifying Product focusing Same district Different district Difference Product diversifying Same district Different district Difference Product focusing–product diversifying Geographic focusing–geographic diversifying

Number of mergers

Number of revaluations

Mean

Median

Standard deviation

46 23

11,051 5569 599 4970 5482 566 4916

34 17

8123 4082 339 3743

17

4041 355 3686

0.318 0.084 0.219 0.115 0.334 0.485 0.613 0.480 0.133 0.401 0.529 0.919 1.013 0.907 0.106 0.240 0.074 0.250 0.176 0.679 0.211

4.368 5.051 5.282 5.022

23

0.686 0.414 0.453 0.409 0.044 0.962 1.086 0.948 0.138 0.548 1.010 1.474 1.788 1.446 0.342 0.541 0.451 0.550 0.099 0.933 0.324

3.522 3.650 3.507

3.697 4.334 4.546 4.314 2.840 2.791 2.844

Panel B of Table 5 ranks the merger sample into two equal-sized groups based on the median value of the premium. As expected, the intra-industry revaluation is more positive when the takeover premium is higher. The mean and median differences between the high and low groups are significant at the 1% level. Lastly, we consider the abnormal returns to bidder and target firms around the merger announcement. The mergers are ranked into two equal-sized groups based on three different criteria: the target CAR, the bidder CAR, and the combined CAR. The combined CAR is a capitalization weighted average of target and bidder CARs based on their equity market values at quarter end prior to the announcement. The results are reported in Panel C of Table 5. For all three abnormal return measures (target, bidder, and combined), higher CARs translate into higher intra-industry revaluations. The mean and median differences are significantly higher at the 1% level across all three measures of abnormal return. Intra-industry revaluation is higher when the bidder and target firms involved in the merger have higher abnormal returns. In the multivariate analysis in later sections, we will control for two other characteristics of merger banks that can influence the magnitude of industry revaluations – prior performance and size. The quality of the information signal can depend on the performance of the bidder bank. Houston and Ryngaert (1994) show that the announcement returns of bidder banks are positively related to profitability. A similar argument can be made about target banks. The intra-industry revaluation from the acquisition of a poorly performing target bank can be different. A second factor is the size of bidder and target banks involved in the merger. New information about the intrinsic values of target banks, and thereby, the intrinsic values of other banks may be proportional to the size of target banks. Further, the size of the target bank relative to the bidder bank can be a useful proxy for the anticipated success of the merger. Loughran and Vijh (1997) find that as the size of the target increases relative to the bidder, mergers tend to generate less value.

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397

4.4. Control variables: competitive effects Competitive forces can also affect the industry reaction to mergers. DeLong (2001) finds that focusing mergers create more value for shareholders of the merged bank. To the extent that geographically and/or product focused mergers enhance the performance of merged banks, the possibility of increased competition can adversely impact non-merger banks. A negative intra-industry revaluation that results from the announcement of a merger reflects the competitive effect. Table 6 categorizes intra-industry revaluations based on the geographic and product focus of the merger. A merger is considered geographically focused if the bidder and target are in the same Federal Reserve districts, and diversifying, otherwise.16 Product focus is determined by differences in asset composition between bidder and target. A Herfindahl-Hirschman index – namely, the sum of the squared differences in asset composition between bidder and target banks is used as a proxy. A lower (higher) value indicates that the asset composition between bidder and target is more (less) similar. The median values of the geographic and product focus proxies are then used to categorize mergers. As shown in Table 6 and 46 of the 80 mergers are geographically focused and the intra-industry revaluation is lower. The differences in the mean (median) values of 0.324 (0.221) are both significant at the 1% level. Moreover, intra-industry revaluations appear to be even lower when the merger is both geographic and product focused. The mean (median) intra-industry revaluation for mergers that are geographic and product focusing is 0.548 (0.401) lower compared to mergers that are geographic focusing and product diversifying. These findings are consistent with a competitive effect. When a merger is both geographically and product focused, it creates a more competitive environment for other banks that reduces the extent of intra-industry revaluations. Surprisingly, the intraindustry revaluation for banks that are in the same district as the target is not significantly different compared to banks that are in a different Federal Reserve district. For the geographically diversifying mergers, the findings are reversed. Product focused mergers are associated with higher intra-industry revaluations compared to product diversifying mergers. This is consistent with economies of scope in product focus. The competitive effect from product focus is, however, mitigated by geographic diversification. Again, there is no significant difference between non-merger banks in the same or different districts as the targets. 4.5. Control variables: spillover effects Intra-industry revaluation can also result from spillover effects such as an increased probability of takeover. We assess this likelihood based on the value of non-merger banks relative to target banks controlling for size and geographic focus. We also considered whether a non-merger bank was subsequently acquired during the 2 years following a given merger announcement as a means of capturing spillover effects, an approach considered by Akhigbe and Madura (1999). This measure did not appear to capture an increased probability of takeover in our study. Instead, the logistic function is used to transform the market-to-book value of the non-merger banks into a probability measure as follows.

LOGMV ¼ 2 

1 x  1 1 þ er

ð3Þ

where x is the equity market-to-book ratio of the non-merger bank and r is the cross-sectional standard deviation at each merger event. Multiplication by 2 and subtracting one converts the transformed variables to a scale from 0 to 1. Since the standard form of the logistic function allows for negative values, this modification is necessary because the equity market-to-book ratio is bounded by a minimum value of zero. The value of LOGMV for non-merger banks is subtracted from the value of the target banks at each merger event for non-merger banks that are in the same asset size quartile as the target bank. The difference captures the degree to which non-merger banks are overvalued or undervalued relative to the target bank. The intra-industry CAR is categorized in Table 7 based on whether the value of LOGMV for the non-merger bank is above or below the value of the target 16

Similar results are found if geography focus is defined by states instead of districts.

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Table 7 Intra-industry revaluations and spillover effects. This table examines intra-industry revaluations surrounding merger announcements associated with spillover effects related to the probability of takeover. We assess this likelihood based on the market value of non-merger banks relative to target banks controlling for size and geographic focus. The logistic function is used to transform the market-to-book value of non-merger banks into a probability measure. The value for non-merger banks is subtracted from the value of the target banks at each merger announcement event for non-merger banks that are in the same asset size quartile as the target bank. The difference captures the degree to which non-merger banks are overvalued or undervalued relative to the target bank. The CARs of non-merger banks are then categorized based on whether the value of the non-merger bank is above or below the value of the target bank and whether the non-merger banks are inside or outside the Federal Reserve districts of target banks.    , , denotes statistical significance at the 1%, 5%., and 10% levels, respectively, based on a (one-sided) standard t-test for differences in means and Wilcoxon test for differences in medians. Same district

Out of district

Overall

Difference

Below target valuation Mean Median Standard deviation N

0.630 0.387 4.052 258

0.848 0.479 4.101 1938

0.822 0.460 4.095 2196

0.218 0.092

Above target valuation Mean Median Standard deviation N

0.434 0.333 3.904 229

0.652 0.390 3.985 2292

0.632 0.350 3.977 2521

0.218 0.723

Overall Mean Median Standard deviation N

0.538 0.155 3.980 487

0.742 0.430 4.039 4230

Difference Mean Median

0.196 0.720

0.196 0.089

0.204 0.275

0.190 0.110

bank and whether the non-merger bank is inside or outside the Federal Reserve districts of the target bank. As expected, intra-industry revaluation is higher when valuation is lower, consistent with a spillover effect related to increased likelihood of takeover. Note also that intra-industry revaluation is higher for out-of-district non-merger banks compared to in-district non-merger banks. For in-district mergers, any spillover effect is partially offset by the competitive effect. 4.6. Multivariate analysis: intra-industry revaluation and opacity The multivariate analysis described in this section evaluates the impact of non-merger bank asset composition, a proxy for opacity, on intra-industry revaluations controlling for exogenous factors. Non-merger bank CARs surrounding each merger announcement are the dependent variables in weighted least squares (WLS) regressions where the inverse of the average standard error for the non-merger bank from the six-factor return generating model is the weight.17 The results of these cross-sectional regressions are presented in Table 8. Model 1 includes nonmerger bank variables and all control variables. Model 2 adds year dummies to Model 1. As a robustness check, Model 3 includes only the non-merger bank variables and controls for differences across merger announcements using only dummy variables (coefficients not reported) for each merger announcement. In all models, opaque asset variables have the expected positive signs and all are statistically significant. The signs of the coefficients are consistent with our expectation from the univariate results in Table 4. Intra-industry revaluation is positively related to investments in opaque assets. If opaque asset variables are all replaced by transparent assets (results not reported), the sign of transparent assets 17

Using OLS or WLS with the log of assets as the weight produces qualitatively similar results.

Table 8 Multivariate analysis: intra-industry revaluations and opacity. This table reports pooled weighted least squares (WLS) regressions of non-merger bank CAR surrounding merger announcements on asset composition and other control factors that are expected to impact intra-industry revaluations. The weights used in the WLS regressions are the inverse of the average standard errors from the 6-factor return generating model used to estimate the intra-industry CAR for non-merger banks. Model 1 includes the non-merger bank variables, deal characteristics, and properties of the merger banks. Model 2 adds year dummies to the variables in Model 1. Model 3 includes the non-merger bank asset composition and controls for merger and time attributes using deal dummy variables (not reported). , ,  denote statistical significance at the 1%, 5%., and 10% levels, respectively. Model 1 Coefficient Intercept

Merger attributes Cash payment Premium Combined firm CAR Two merger dummy Target profitability Target equity M/B Target log of assets Bidder profitability Bidder equity M/B Bidder log of assets Relative size Competitive Effects Geo. and product focus Geo. div. and product focus Spillover effects Valuation relative to target Same size as target Same district as target Valuation relative to target  (Same size as target)  (Same district as target) Valuation relative to target  (Same size as target)  (Diff. district as target) Year dummies Merger dummies Adjusted R2 F-value N

0.044

0.09

Model 3

Coefficient 

t-Statistic

Coefficient

6.71 2.85 4.54 2.27 2.34

1.069

2.12

2.007

0.671 1.356 1.129 2.763

1.197 1.918 1.793 5.594

4.54 5.71 3.21 4.31

0.851 1.501 1.202 5.068

3.29 4.55 2.19 3.91

0.016 0.367 14.891 0.416 4.828 0.725 0.554 22.706 0.529 0.464 3.208

0.16 2.61 24.80 3.00 1.01 10.46 10.87 4.12 11.12 9.01 11.19

0.027 0.063 16.789 0.165 43.255 0.544 1.036 10.843 0.567 0.746 4.729

0.26 0.44 26.91 1.07 8.66 6.88 19.65 1.92 11.63 14.31 16.39

0.429 0.409

5.69 5.18

0.428 0.154

5.60 1.92

0.266 0.071 0.095 3.090 1.411 No No 0.0708 67.36 19,174

1.14 1.07 0.99 2.23 3.23

0.248 0.067 0.122 2.833 1.358 Yes No 0.1218 95.95 19,174

1.07 1.04 1.30 2.10 3.19

t-Statistic



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Bank variables Real-estate loans Other loans Other opaque assets Capital

Model 2 t-Statistic

No Yes 0.2709 86.83 19,174 399

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is significantly negative. As might also be expected, the negative sign of capital indicates that leverage amplifies the equity revaluations. Control variables in the regressions have the anticipated signs and most are statistically different from zero. Among the merger-specific characteristics, the coefficients of the premium and the combined firm CAR are positive and significant in Model 1, as expected from the univariate results in Table 5. The dummy variable that controls for the four cases in which a merger is announced on exactly the same day is positive and significant, indicating that multiple merger announcements have a positive impact on the intra-industry revaluation of non-merger banks. The signs and significance of the merger bank performance variables suggest that intra-industry revaluation is greater when the target is low performing or the bidder is high performing. The size of the merger banks also appears to matter. As expected, larger target banks induce higher intra-industry revaluations. Moreover, when the bidder is large, intra-industry revaluation is lower. Finally, intra-industry revaluation is lower when the target bank is large relative to the bidder. Competitive effects are captured by the interaction of geography and product focus dummy variables. The statistically significant negative sign for the geographic focus and product focus dummy variable is consistent with a competitive effect. Similarly, the statistically significant positive sign for the geographic diversification and product focus dummy variable is consistent with Table 6. Spillover effects associated with the probability of takeover are captured using three characteristics of non-merger banks. The valuation relative to the target is equal to LOGMV of the target minus LOGMV of the non-merger bank, where LOGMV is computed according to (3). Dummy variables are used to control for the size and Federal Reserve district of the non-merger bank relative to the target. By themselves, each variable has little effect on the magnitude of intra-industry revaluations. However, the interactions of the valuation relative to the target and the same size of the target with both the inside and outside district dummy are positive and significant. To assess the economic contribution of opacity to the intra-industry revaluations, note that the coefficient of 1.197 for real estate loans in Model 1 of Panel A suggests that if a non-merger bank has 1% more in real estate loans, average revaluation will increase by 0.0001197 percentage points – approximately 1.2 basis points over an 11-day event window. 4.7. Robustness tests To strengthen our conjecture that opacity contributes to intra-industry revaluation, we consider split bond ratings as an alternative measure of opacity. Split bond ratings reflect disagreements among major bond rating agencies about the true risk of a firm (Morgan, 2002; Livingston et al., 2007). We compile data on Moody’s and Standard and Poor’s bond ratings from Thomson Financial (SDC) for newly issued subordinated debt (excluding mortgage debt) by banks over the period 2000–2006. We aggregate the rating data by quarter for each bank and compute a quarterly average. The quarterly rating information for each is assigned to one of four categories: (i) dual-rated, split rating, (ii) dualrated agreed rating, (iii) single-rated by only one agency, or (iv) unrated by either agency. A fifth possibility is that a bank did not issue any debt during the quarter. We repeat the regression in Table 8 using split bond ratings as explanatory variables in place of asset composition. For those bank-quarters classified as dual-rated, we include the average percent of split-rated issues and the average magnitude of the split. We also include dummy variables that equal one if the bank has single-rated debt or unrated debt. The results are presented in Table 9. The findings in Table 9 are consistent with those that use asset composition as the proxy for opacity. In particular, intra-industry revaluation around mergers is positively related to the occurrence and magnitude of split ratings, as the signs for both the average percent of split ratings and the average magnitude of the split are positive and significant in Models 1 and 2, respectively. Interestingly, single-rated debt does not affect the magnitude of revaluation by a statistically significant amount, but unrated debt does result in a significantly larger revaluation. The robustness test supports our contention that intra-industry revaluations around merger announcement are higher for non-merger banks that are more opaque. In unreported results, we also examine if intra-industry revaluations of non-merger banks are affected by whether or not the bidders are serial, or ‘repeat’, acquirers. Our sample of 80 merger

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Table 9 Robustness: intra-industry revaluation and alternative opacity proxies. This table reports pooled weighted least squares (WLS) regressions of non-merger bank CAR surrounding merger announcements on alternative proxies for opacity. The weights used in the WLS regressions are the inverse of the average standard errors from the 6-factor return generating model used to estimate the intra-industry CAR for non-merger banks. Split rating variables are computed as the average occurrence and magnitude of split ratings on new bond issues each quarter. Sing-rated debt is a binary variables equal to one if a bank issued debt during the quarter that was only rated by either Moody’s or S&P. Unrated debt is a binary variable equal to one if a bank issued debt during the quarter that was not rated by either rating agency. , ,  denote statistical significance at the 1%, 5%., and 10% levels, respectively. Model 1

Model 2

Coefficient

t-Statistic

Coefficient

t-Statistic

Intercept Split ratings Average % split rated Average magnitude of split Single-rated debt Unrated debt

0.429

0.90

0.424

0.89



4.86

0.218 0.934

0.24 2.99

0.258 0.204 0.921

3.68 0.22 2.95

Capital

3.974

3.09

4.113

3.20

Merger attribute controls Competitive effect controls Spillover effect controls Year dummies Adjusted R2 F-Value N

Yes Yes Yes Yes 0.1222 96.29 19,174

Yes Yes Yes Yes 0.1217 95.88 19,174

0.527

announcements involved 49 bidder banks, of which 14 are ‘repeat’ and 35 are ‘one-time’ acquirers. Allotting the overall sample of merger announcements into ‘repeat’ and ‘one-time’ acquirer subsamples, the magnitudes of intra-industry revaluations for merger announcements involving ‘repeat’ and ‘one-time’ bidders are quantitatively similar across both groups and to the overall sample of merger announcements. We also re-estimated seemingly unrelated regressions of the models in Tables 8 and 9 using separate asset composition and split rating variables for non-merger banks according to whether the merger involved a ‘repeat’ or ‘one-time’ bidder. For both groups, we find that intraindustry revaluations are still significantly higher for non-merger banks that have larger investments in opaque assets and are either split-rated or unrated. Furthermore, the impact on intra-industry revaluations of non-merger banks of investments in opaque assets and split-rated or unrated, is similar regardless of whether the bidder is a ‘repeat’ or ‘one-time’ acquirer. For single-rated non-merger banks, however, intra-industry revaluations are negative and insignificant in mergers involving ‘repeat’ bidders, but positive and significant in mergers involving ‘one-time’ bidders. 5. Revaluations during periods of financial instability Between 2000 and 2006, merger announcements have, on average, a positive effect on the values of other banks. More importantly, this positive intra-industry revaluation effect is larger for banks with higher investments in opaque assets. But does opacity also increase the magnitude of share price declines when there is bad news? 5.1. Opacity and financial instability We utilize the bank-specific coefficients from the six-factor return-generating model estimated over the period 1999–2006 to compute out-of-sample daily abnormal returns over the period January 2007 through June 2008. The cumulative daily abnormal returns capture the share price reversals. The results are presented in Table 10 for the non-merger banks included in Table 4 that remain operating entities as of June 30, 2008. Panel A results are for non-merger banks that were present throughout the entire sample period; and Panels B and C, for non-merger banks that were present in the first and second halves of the sample period, respectively. Categorization is based on quartile

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Table 10 Univariate analysis: opacity and financial instability. This table examines the impact of opacity on intra-industry revaluations surrounding merger announcements and the subsequent CAR of the non-merger banks in the post-sample period January 2007 through June 2008 (the ‘‘financial crisis’’). Non-merger banks are sorted into quartiles based on their level of transparent assets. Financial crisis CARs are computed as the sum of daily abnormal returns for each non-merger bank during the financial crisis period. The daily abnormal returns are calculated using the beta coefficients from the 6-factor return generating model used to compute intra-industry revaluations in Table 3. Only non-merger banks that continue to be exchange listed as of June 30, 2008 are included. , ,  denote statistical significance at the 1%, 5%, and 10% levels, respectively based on a standard t-test for differences in means and a Wilcoxon test for differences in medians.

Quartile

N

Cumulative CAR merger announcement (2000–2006)

Cumulative CAR post-period (January 2007–June 2008)

Mean

Median

Mean

Median

Panel A: non-merger banks from entire period Most opaque 1 27 87.60 2 24 70.43 3 27 65.76 Least opaque 4 27 50.53 Most opaque–least opaque 37.07

92.61 71.24 82.86 50.90 41.71

49.35 27.08 15.95 11.06 60.41

51.10 32.31 11.44 17.99 69.09

Panel B: non-merger banks from January 2000–June 2003 Most opaque 1 29 72.24 2 24 56.57 3 35 54.46 Least opaque 4 31 42.98 Most opaque–least opaque 29.26

73.77 55.33 65.11 45.91 27.86

42.38 24.08 22.64 8.92 51.30

49.07 21.33 22.14 5.11 54.18

Panel C: non-merger banks from July 2003–January 2006 Most opaque 1 38 18.95 2 36 12.87 3 39 13.40 Least opaque 4 35 7.95 Most opaque–least opaque 11.00

19.01 17.14 11.53 11.54 7.47

62.00 36.42 15.37 14.96 76.96

61.62 44.06 6.52 28.56 90.18

rankings in Panel B of Table 4. For comparative purposes, the cumulative intra-industry revaluations around merger announcements in 2000–2006 are also reported. The pattern shown in all three panels is clear. The quartile containing the most opaque banks realized the highest positive revaluations around merger announcements but also suffered the largest price reversals during the financial crisis. The differences between the most and least opaque quartiles are statistically significant in all three panels. For adverse events, the magnitude of the negative abnormal returns is directly related to a bank’s investments in opaque assets. Cross-sectional multivariate analysis in Table 11 confirms this relationship, and not surprisingly, real-estate loans are the most significant. Opacity can exacerbate bubbles and crashes that contribute to financial instability.

5.2. 2 Feedback effects An interesting question that arises from our results is whether the positive price signals about the value of opaque assets surrounding merger announcements influenced the risk-taking behavior of banks. Positive price signals can encourage banks to increase their investments in opaque assets. We divided the banks that were not involved in mergers into quartiles based on the cumulative revaluation around merger announcements during the first half of the sample period. We plot the average percent of transparent assets throughout the sample period for each quartile in Fig. 3. As expected, the quartile with the lowest (highest) revaluation has more (less) transparent assets, with all quartiles showing a downward trend. But the decline in transparent assets from 2000 to 2006 for the quartile of banks experiencing the lowest level of revaluation during the first half of the sample period is significantly greater compared to the quartile with the highest level of revaluation. This demonstrates how price contagion creates a feedback effect that influences the real activities of banks. Recognizing that banks with more opaque assets received higher valuations, banks with the fewest opaque assets

Intercept Asset composition Real estate loans Other loans Other opaque assets Operating attributes Capital Profitability Non-performing loans Core deposits Non-interest income Log of Assets Adjusted R2 F-value N

Non-merger banks from entire period

Non-merger banks from first half

Non-merger banks from second half

Coefficient

Coefficient

Coefficient

t-Stat.

108.80

1.11

t-Stat.

78.74

0.61

177.08 88.27 280.62

3.32 1.03 1.61

36.78 155.48 5401.30 136.37 2415.62 5.88 0.3258 6.58 105

0.10 0.19 2.72 2.19 2.73 0.78

Standardized coefficient

t-Stat.

Standardized coefficient

Standardized coefficient

26.23

0.26

0.370 0.110 0.207

194.00 89.19 252.51

3.84 1.07 1.89

0.410 0.111 0.248

209.27 137.37 224.50

4.43 1.89 1.43

0.424 0.178 0.151

0.009 0.018 0.254 0.259 0.292 0.106

131.46 27.34 6471.09 118.56 1688.45 4.02 0.3125 6.96 119

0.39 0.04 3.55 2.06 2.16 0.66

0.033 0.003 0.30.9 0.228 0.232 0.093

14.67 231.67 4256.12 154.76 1258.60 9.36 0.2867 7.56 148

0.05 0.33 2.66 3.24 2.04 1.59

0.003 0.026 0.204 0.285 0.178 0.185

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Table 11 Multivariate analysis: opacity and financial instability. This table examines the impact of opacity on the CAR of non-merger banks during the 2007–2008 financial crisis. Weighted least squares regressions (WLS) of financial crisis CARs on asset composition and operating attributes as of December 31, 2006 are estimated using the inverse of the average standard errors of daily abnormal returns in the merger announcement period as weights. Computations of financial crisis CARs are described in Table 10 and all independent variables are described in Appendix A. Only non-merger banks that still exist as of June 30, 2008 are included. Standardized regression coefficients reflect the effect of a standard deviation change in the explanatory variable. , ,  denotes statistical significance at the 1%, 5%., and 10% levels, respectively.

403

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Fig. 3. Change in transparent assets by first half revaluation quartiles. The above chart plots the average percent of transparent assets for quartiles created by ranking the cumulative intra-industry revaluation of non-merger banks during the first half of the sample period. A bank must be a non-merger bank for all 80 of the merger announcements in order to be included. The mean and median changes in the percent of transparent assets within each quartile are presented below the chart. , ,  denotes statistical significance at the 1%, 5%., and 10% levels, respectively. These results show that non-merger banks whose valuations benefitted the least from merger activity in the first half of the period reduced their transparent assets the most in the second half of the period.

(and lowest revaluations) appear to have emulated the strategies of the more opaque banks by making relatively larger investments in opaque assets from 2000 to 2006.

5.3. 3 Composition of equity risk A consequence of the increased investments in opaque assets over this time period is a change in the composition of risk. Lack of transparency reduces the idiosyncratic component of risk as firm-specific information becomes less important and/or less reliable (Morck et al., 2000). We compute quarterly bank betas with the market model using weekly equity returns and the CRSP equal-weighted index as the market proxy over the period January 2000 through June 2008. Weekly returns are computed from Wednesday to Tuesday to minimize the impact of documented market anomalies associated with weekends. As Fig. 4 demonstrates, bank betas clearly increased over this period. Furthermore, idiosyncratic risk steadily declined until mid-2007, and spiked upward with the onset of the financial crisis.

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405

Fig. 4. Systematic and idiosyncratic risk. The graph above shows the systematic and idiosyncratic risk computed with weekly returns by quarter using the market model with the CRSP equal-weighted index as the market proxy. Plotted values reflect the median across all banks in the sample. Systematic risk is captured by beta, and idiosyncratic risk is captured by the absolute value of the market model residual.

6. Conclusion Deposit insurance and bank regulation temper the impact of bank opacity on the occurrence of depositor bank runs. But opacity can still create financial instability by contributing to market bubbles and crashes because contagion is a characteristic of the price discovery process in opaque industries. Pricing is inefficient because opacity often prevents investors from accurately assessing bank-specific risks. Industry-wide market reactions to bank-specific news can create price volatility that contributes to financial instability when valuation fails to constrain, and at times even encourages, excessive risktaking. We empirically examine the contagion effect by measuring the impact of bank merger announcements in the period 2000–2006 on the valuation of banks that were not involved in mergers. An overall upward revision in the total market value of non-merger banks is observed, but more importantly, revaluation is higher for non-merger banks that have larger investments in opaque assets. The findings are robust to controls for the effects related to the possibility of heightened competition, probability of takeover from bank mergers, changes in real estate prices, and the level of interest rates. In addition, we assess the impact of opacity on bank share price declines during the period January 2007 through June 2008. Non-merger banks that benefited most from merger activity in 2000–2006 experienced the largest declines in equity during the 2007 financial crisis. Banks appear to have increased investments in opaque assets in response to the positive price signals around mergers, which exacerbated the eventual decline in equity. This trend is more pronounced for those banks experiencing the lowest revaluation in the first half of the sample period. The shift into more opaque assets also appears to have changed the composition of equity risk, as there is a notable increase in systematic risk and corresponding decrease in idiosyncratic risk throughout the sample period. The contagion effects from bank opacity are unlikely to be dampened by current regulatory oversight. Even to the extent that regulators have inside information about the creditworthiness of a bank’s asset portfolio, most or all of that information remains confidential, so it is unhelpful to inves-

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tors. Moreover, it is unlikely regulators have a competitive advantage over private analysts in piercing the portion of opacity related to inherent asset complexity. Reducing opacity will require enhanced information disclosure and/or a reduction in asset complexity, each of which has costs and benefits that must be weighed by policymakers.

Acknowledgments We appreciate the comments of Daniel Pu Liu, Tomas Jandik, Mark Flannery, and Don Morgan. We also thank seminar participants at the University of Arkansas, the 2009 Financial Management Association European Conference, and the 2011 Southwest Finance Symposium at the University of Tulsa. Research support was provided in part by the James H. Penick Bank of America Research Fund at the University of Arkansas. This paper was previously circulated under the title Price Discovery in Opaque Markets.

Appendix A See Tables A1 and A2

Table A1 Financial variable definitions. The financial variables used in this study taken from the FR Y-9C call reports are defined below. Balance sheet items are the end of quarter values. Income statement items are annualized quarterly values. All variables are scaled by total assets unless otherwise specified. Items with an  indicates that these values were reduced by an estimated allowance for losses (BHCK3123) based on the amount of loans outstanding. Assets

Total inflation-adjusted assets

Real estate loans

Commercial and residential real estate loans and leases, net  Other loans All other loans, net Other opaque All other opaque assets: trading assets, assets fixed assets, intangible assets, other assets, investment in unconsolidated subsidiary, other real estate owned, and opaque available-for-sale and held-tomaturity securitiesa Transparent assets All transparent assets: cash, federal funds sold, securities purchased under agreements to resell, guaranteed AFS and HTM securities Capital Equity capital Profitability Earnings before taxes and extraordinary items Non-interest Noninterest income income Non-performing Nonperforming loans, defined as nonloans accruing loans or those greater than 90 days past due Core deposits Core deposits

Equity M/B

a

Ratio of market-to-book value of equity. Market value of common equity (shares outstanding  price) is computed as shares outstanding  |PRC| from CRSP

BHCK2170 BHDM1415 + BHDM1420 + BHDM1460 + BHDM1480 + BHDM1797 + BHDM5367 + BHDM5368 BHCK2122  Real Estate Loans Assets–Real Estate Loans–Other Loans–Transparent Assets

BHCK0081 + BHCK0395 + BHCK0397 + BHCK1350 + BHDMB987 + BHCKB989 + BHCK1754 + BHCK1773  BHCK1709  BHCK1713  BHCK1733  BHCK1736  BHCKC026  BHCKC027 1  (BHCK2948/ASSETS) BHCK4301 BHCK4079  BHCKA220 BHCK5525 + BHCK5526

BHCB2210 + BHCB3187 + BHCB2389 + BHCB6648 + BHOD3189 + BHOD3187 + BHOD2389 + BHOD6648  BHDMA243  BHDMA164 Shares outstanding  price/book value of common equity

Opaque securities are those that do not have an explicit or implicit guarantee from a federal government-related entity.

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407

Table A2 Control variable definitions. Variable

Description

Cash Payment

A dummy variable equal to one if the payment to target shareholders consists of at least 75% cash The premium paid to the target, expressed as a percentage, based on the price paid and the stock price of the target 28 calendar days prior to the announcement The cumulative abnormal return of the target bank over a ±5 day event window The cumulative abnormal return of the bidder bank over a ±5 day event window The combined announcement CAR of the target and bidder based on the relative total market value of equity as of the quarter ended prior to the announcement A dummy variable equal to one if there are two mergers announced on exactly the same day. The larger of the two mergers, based on the asset size of the target, is retained in the sample Earnings before taxes and extraordinary items of the target Market-to-book value of equity for the targe Earnings before taxes and extraordinary items of the bidder Market-to-book value of equity for the bidder Natural log of target inflation-adjusted assets Natural log of bidder inflation-adjusted assets Relative size of the target to the bidder, computed as target assets divided by bidder assets A dummy variable equal to one if the merger is considered geographically focusing and product focusing, as described in Table 6 A dummy variable equal to one if the merger is considered geographically diversifying and product focusing, as described in Table 6 The logistic-transformed market-to-book value of equity. The non-merger bank LOGMV subtracted from LOGMV of the target A dummy variable equal to one if the non-merger bank is in the same asset quartile as the target A dummy variable equal to one if the non-merger bank is in the same Federal Reserve district as the target

Premium Target CAR Bidder CAR Combined firm CAR Two merger dummy

Target profitability Target equity M/B Bidder profitability Bidder equity M/B Target size Bidder size Relative size Geo. and product focus Geo. div. and product focus LOGMV Valuation relative to target Same size as target Same district as target

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